code: def get_target_dim(quant_type, quant_scheme):
      # for weight: c_out x c_in x (h) * (w)
      # for feature maps: batch * channel * (t) * h * w
      # other type is not supported for now
      default_idx = 0 if quant_type == QuantType.WEIGHT else 1
      if is_per_channel(quant_scheme):
          target_dim = default_idx
      else:
          target_dim = None
      return target_dim
  
file_path: nni/nni/compression/utils/quantization/utils.py
imports: import torch
  from nni.common.version import TORCH_VERSION
  from .literal import QuantDtype, QuantScheme, QuantType
label_desc: Use image node_id 3 for calling a global function with example usage: get_target_dim(quant_type, quant_scheme) and returns: target_dim